{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "237.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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1dOae1CBn7kkNMvhSgwy+1CCDLzXI4EsNMvhSgwy+1CCDLzXo/wBb97OwgTi6yQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0ebb7c390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "236.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0ebb04400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "237.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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uugAk3STpbdfvA+8HTlDn+9nnHRz3Az+m0RP+ZR07WZpq+QYwCVyl0fttp9ETjgNngH8BltVU2900esIXgOPFz/2DUB/wXuBYUdsJ4K+K+b8BPAucBb4JLK7xvd0EHBiUuooani9+Tl7/v1/n++kz98wS8pl7Zgk5+GYJOfhmCTn4Zgk5+GYJOfhmCTn4Zgk5+GYJ/T9l4/AHQ5BNJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0ebb22668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "236.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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55Z7UIIMvNcjgSw0y+FKDDL7UIIMvNcjgSw0y+FKD/hdxhbMsRYzs1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0eba80630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "237.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0ebaa47f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "237.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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Y1lBtt9PpCV8AjhZ/7m5DfcBvAEeK2o4Bf1as/2XgWeAU8BVgcYOv7SbgqbbUVdTwfPHn+JX/+02+np65Z5aQZ+6ZJeTgmyXk4Jsl5OCbJeTgmyXk4Jsl5OCbJeTgmyX0//vR16xNw3WwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0eb9f2ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "237.0\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0eb9aea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "236.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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Sgwy+1CCDLzXI4EsNMvhSg/4H17TKiM1ZHcQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0eb9cf1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "237.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0eb9a1080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1, 10):\n",
    "    file_path = \"/home/jachinshen/Downloads/fireNumber/{}.jpg\".format(i)\n",
    "    img = cv.imread(file_path)\n",
    "    img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n",
    "    img = img[:,int(img.shape[1] * 0.2) : int(img.shape[1] * 0.8)]\n",
    "    img = cv.GaussianBlur(img, (51, 51), 0)\n",
    "    _, maxVal, _, _ = cv.minMaxLoc(img)\n",
    "    _, img = cv.threshold(img, 0.80 * maxVal, 255, cv.THRESH_BINARY)\n",
    "    print(maxVal)\n",
    "    img = cv.resize(img, (56, 56))\n",
    "    plt.imshow(img)\n",
    "    cv.imwrite(\"{}.jpg\".format(i), img)\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
